New method to find novel connections from gene to gene, drug to drug and between scientists

July 24, 2012

Researchers from Mount Sinai School of Medicine have developed a new computational method that will make it easier for scientists to identify and prioritize genes, drug targets, and strategies for repositioning drugs that are already on the market. By mining large datasets more simply and efficiently, researchers will be able to better understand gene-gene, protein-protein, and drug/side-effect interactions. The new algorithm will also help scientists identify fellow researchers with whom they can collaborate.

Led by Avi Ma'ayan, PhD, Assistant Professor of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine, and Neil Clark, PhD a postdoctoral fellow in the Ma'ayan laboratory, the team of investigators used the new algorithm to create 15 different types of gene-. They also discovered novel connections between drugs and side effects, and built a collaboration network that connected Mount Sinai investigators based on their past publications.

"The algorithm makes it simple to build networks from data," said Dr. Ma'ayan. "Once high dimensional and complex data is converted to networks, we can understand the data better and discover new and significant relationships, and focus on the important features of the data."

The group analyzed one million of patients to build a network that connects commonly co-prescribed drugs, commonly co-occurring side effects, and the relationships between side effects and combinations of drugs. They found that reported side effects may not be caused by the drugs, but by a separate condition of the patient that may be unrelated to the drugs. They also looked at 53 and connected them to 32 severe side effects. When chemotherapy was combined with cancer drugs that work through cell signaling, there was a strong link to cardiovascular related adverse events. These findings can assist in post-marketing surveillance safety of approved drugs.

The approach is presented in two separate publications in the journals BMC Bioinformatics and BMC Systems Biology. The tools that implement the approach Genes2FANs and Sets2Networks can be found online at http://actin.pharm.mssm.edu/genes2FANs and http://www.maayanlab.net/S2N.

Explore further: Researchers identify promising new drug target for kidney disease

Related Stories

Cancer's next magic bullet may be magic shotgun

June 15, 2012

A new approach to drug design, pioneered by a group of researchers at the University of California, San Francisco (UCSF) and Mt. Sinai, New York, promises to help identify future drugs to fight cancer and other diseases that ...

Recommended for you

Success in the 3-D bioprinting of cartilage

April 28, 2017

A team of researchers at Sahlgrenska Academy has managed to generate cartilage tissue by printing stem cells using a 3-D-bioprinter. The fact that the stem cells survived being printed in this manner is a success in itself. ...

Mouse teeth providing new insights into tissue regeneration

April 27, 2017

Researchers hope to one day use stem cells to heal burns, patch damaged heart tissue, even grow kidneys and other transplantable organs from scratch. This dream edges closer to reality every year, but one of the enduring ...

Dentistry research ID's novel marker for left-handedness

April 27, 2017

Individuals with a slender lower face are about 25 percent more likely to be left-handed. This unexpected finding was identified in 13,536 individuals who participated in three national surveys conducted in the United States.

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.